
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


A__							: Optional[int]     =       logging.get_logger(__name__)

A__							: Optional[Any]     =       {'tokenizer_file': 'tokenizer.json'}

A__							: List[str]     =       {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}





class     lowercase							(	_A       ):
					__a											=     VOCAB_FILES_NAMES
					__a											=     PRETRAINED_VOCAB_FILES_MAP
					__a											=     ["""input_ids""", """attention_mask"""]
					__a											=     None
					def __init__(		self			,						SCREAMING_SNAKE_CASE__=None			,						SCREAMING_SNAKE_CASE__=None			,						SCREAMING_SNAKE_CASE__=None			,						SCREAMING_SNAKE_CASE__="<unk>"			,						SCREAMING_SNAKE_CASE__="<s>"			,						SCREAMING_SNAKE_CASE__="</s>"			,						SCREAMING_SNAKE_CASE__="<pad>"			,						SCREAMING_SNAKE_CASE__=False			,						SCREAMING_SNAKE_CASE__=False			,						**SCREAMING_SNAKE_CASE__			,						):
								"""simple docstring"""







								super().__init__(
								    UpperCamelCase__			,						UpperCamelCase__			,						tokenizer_file=UpperCamelCase__			,						unk_token=UpperCamelCase__			,						bos_token=UpperCamelCase__			,						eos_token=UpperCamelCase__			,						pad_token=UpperCamelCase__			,						add_prefix_space=UpperCamelCase__			,						clean_up_tokenization_spaces=UpperCamelCase__			,						**UpperCamelCase__			,						)
								lowerCAmelCase__			:     Dict					   =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
								if pre_tok_state.get('''add_prefix_space'''			,						UpperCamelCase__     ) != add_prefix_space:
											lowerCAmelCase__			:     Union[str, Any]					   =      getattr(UpperCamelCase__			,						pre_tok_state.pop('''type'''     )     )
											lowerCAmelCase__			:     List[Any]					   =      add_prefix_space
											lowerCAmelCase__			:     Union[str, Any]					   =      pre_tok_class(**UpperCamelCase__     )

								lowerCAmelCase__			:     int					   =      add_prefix_space
					def lowercase_      (		self			,						*SCREAMING_SNAKE_CASE__			,						**SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Any					   =      kwargs.get('''is_split_into_words'''			,						UpperCamelCase__     )
								if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
											    ''' pretokenized inputs.'''     )

								return super()._batch_encode_plus(*UpperCamelCase__			,						**UpperCamelCase__     )
					def lowercase_      (		self			,						*SCREAMING_SNAKE_CASE__			,						**SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Any					   =      kwargs.get('''is_split_into_words'''			,						UpperCamelCase__     )

								if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
											    ''' pretokenized inputs.'''     )

								return super()._encode_plus(*UpperCamelCase__			,						**UpperCamelCase__     )
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__ = None     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Optional[int]					   =      self._tokenizer.model.save(UpperCamelCase__			,						name=UpperCamelCase__     )
								return tuple(UpperCamelCase__     )
					def lowercase_      (		self			,						SCREAMING_SNAKE_CASE__     ):
								"""simple docstring"""







								lowerCAmelCase__			:     Optional[int]					   =      []
								for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(UpperCamelCase__			,						add_special_tokens=UpperCamelCase__     ) + [self.eos_token_id]     )

								if len(UpperCamelCase__     ) > self.model_max_length:
											lowerCAmelCase__			:     str					   =      input_ids[-self.model_max_length :]
								return input_ids





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

